Correlation Matrix (Numerical) | Feature Selection | Python

[@alterafpga4349]

good video, thanks

[@HackersRealm]

Glad it was helpful!!!

[@renxioxland5684]

Ty so much for the video, this helps me a lot to understand the concept

[@HackersRealm]

Glad it helps!!!

[@maryamshahzadi3364]

this video helped me thanks

[@HackersRealm]

Glad it was helpful😄

[@sweetyrani6727]

could you please explain how dimension reduction done using best first search with an example of correlation matrix .

[@tinytrip6844]

HI Aswin bro, what does it meant the term you used "its leaking the data" @4.48 sec.

[@tinytrip6844]

hi aswin bro, values more than 0.05? or more than 0.5 corr dependies with each other? @9.05 sec

[@HackersRealm]

it depends on the range, if it's closer to 1, it's highly correlated,, if it's in middle like 0.5, there is medium correlation, etc.,

[@MrRafaelSencio]

By doing feature selection with the correlation matrix, we may ignore interactions between features that were not captured by this method.

[@karthikrajendran3394]

What should we do with -ve correlated columns?

[@HackersRealm]

positive and negative both are to be considered which is at extremes.

[@vijaynath1986]

Hi, Video did not clearly say, which column to drop and which one to keep. No conclusion. More info could be better